Multispectral and hyperspectral meibography

ABSTRACT

A method performed by a multispectral or hyperspectral meibomian gland imaging device is disclosed, which comprises directing light having visible and infrared spectra from a broadband illuminator toward an everted eyelid; forming images of the everted eyelid using an imaging system; recording the images using a detection system, which separates the images into spectral channels, which include at least one visible spectral channel and at least one infrared spectral channel; displaying the recorded images to adjust the position of the subject to optimize image quality; digitally processing the recorded images to obtain spatial and spectral information; evaluating the health of at least one meibomian gland of the eyelid by analyzing the information. Also disclosed is a multispectral or hyperspectral meibomian gland imaging device, comprising a broadband illuminator directing light that covers visible and infrared spectra; an imaging system, and a detection system.

FIELD OF THE INVENTION

This invention relates to the field of ophthalmology, in particular, toimage and evaluate the health of meibomian glands.

BACKGROUND OF THE INVENTION

Dry eye syndrome is one of the most common reasons people visit anophthalmic hospital. Dry eye syndrome occurs when patients either cannotsecrete enough tears or their tear quality is not good enough. Tear filmis a thin, moist film anterior to the corneal epithelial cells, whichforms the interface of an eye with the ambient air, and it's essentialto ocular surface health. Tear film thickness is reported to be around 3μm. Tear film consists of three layers from posterior to anterior: themucous layer, the aqueous layer, and the lipid layer. The mucous layerforms a viscoelastic matrix to stabilize the tear film on the cornealepithelium, and the mucins are secreted by the goblet cells in theconjunctiva. The aqueous layer is the major watery part of the tearfilm, which contains a number of nutrients and proteins. The aqueouslayer is secreted from the lacrimal gland, and it is essential forspreading of the tear film and regulating tear osmolarity. The anteriorlipid layer thickness is usually in the order of 1 to 100 nm. Lipids aresecreted by the meibomian glands, which are embedded in the posteriortarsal plate. The lipid layer serves to reduce evaporation, and improvethe tear film stability.

Meibomian glands are modified sebaceous glands, each about 3˜4 mm inlength, approximately vertically lined up inside the tarsal plates ofboth eyelids. There are about twenty-five to forty meibomian glands inthe upper eyelid and about twenty to thirty in the lower eyelid.Meibomian glands are holocrine glands with racemose morphology. Eachmeibomian gland has a tubuloacinar structure, comprising a central ductwith an orifice adjacent to the mucocutaneous junction of the eyelid,and acini connected to the central duct through ductules. The functionalcells of a meibomian gland are meibocytes, which are modified sebaceouscells, forming clusters in each acinus. Each meibocyte synthesizes andsecretes lipids, also known as meibum. During each blink, the muscle ofRiolan may compress the meibomian gland ductules, causing meibumsecretion. The secreted meibum passes through the ductule, the lumen ofthe central duct, and forms the lipid layer of the tear film afterexiting through the orifice of a meibomian gland.

Generally speaking, dry eye syndrome can be divided into two maincategories, aqueous deficient dry eye and evaporative dry eye. Meibomiangland dysfunction is the most common form of evaporative dry eye.Abnormality in the meibum secretion, or the obstruction of the orificeof the central duct could lead to meibomian gland dysfunction. Chronicmeibomian gland dysfunction might result in meibomian gland atrophy.

Meibography is a noninvasive, in vivo imaging technique to directlypresent meibomian gland morphology. Usually, an eyelid is everted withfingers or an eyelid everter to expose the palpebral conjunctivalsurface to an imaging device. Meibography uses the differences inabsorption, translucency, reflectance and scattering of light by themeibomian glands, compared to adjacent tissues, to form an image withhigh enough contrast to examine the meibomian glands distribution withinthe upper and lower eyelids. Traditionally, there are two types ofmeibography, one with transillumination, and the other with directillumination. With transillumination, an eyelid is everted over anilluminator, and the light transmitted through the eyelid is imaged.With direct illumination, an everted eyelid is directly illuminated byan infrared light source in a non-contact manner, and the reflectedlight off the eyelid is imaged.

Meibography was invented by Tapie in 1977 based on clinical tests.Later, infrared light was used to enhance the contrast. The earlymeibographers usually employed transillumination. In 2008, Arita et al.reported a non-contact meibography system that uses direct illuminationto collect reflection images of the everted eyelid. Later, moredirect-illumination, non-contact meibographers have been developed,partly because the direct-illumination type is more comfortable for thepatients during measurements compared with the earlier transilluminationtype.

Further, images of the lid margin have been used to directly inspectindividual meibomian gland orifices at the inner side of the eyelidsclose to the base of eyelashes. Obstruction of the meibomian glandorifices by solidified lipids might lead to meibomian gland dysfunction,or even meibomian gland atrophy.

However, in the prior art, meibography is usually done with a limitedspectrum, most commonly using near infrared light, such as described inU.S. Pat. Nos. 8,249,695, 8,600,484, 9,320,439, and U.S. Pat.Application 20110273550, and the final resultant image of the meibomianglands are black and white grayscale images. Even if pseudocolor imagesare used, the pseudocolor images are generated by assigning differentcolors to different gray levels of the black and white images, or thepseudocolor images are used to denote temperature difference in thermalimaging. These grayscale-based pseudocolor images do not containspectral properties, and could not provide more information fordiagnosis than the original grayscale image. The restriction of thelight in the infrared spectrum limits the retrievable information of themeibomian gland images and potentially loses some critical pathologicalinformation.

Multispectral and hyperspectral imaging systems combine imaging withspectroscopy, hence both spatial and spectral properties of the objectcould be investigated.

Multispectral imaging captures image data of certain object areas inseveral spectral bands. Usually spectral filters and different detectorsthat are sensitive to particular wavelength ranges are employed togenerate a number of spectral bands. The total number of spectral bandsis usually fewer than 20.

Hyperspectral imaging has a lot more spectral channels thanmultispectral imaging and usually hundreds of spectral channels areused. Each resultant hyperspectral image is a three-dimensional datacube, known as a “hypercube”, with two spatial dimensions (x, y), andone spectral dimension λ.

Even though there is no consensus on the number of spectral bands toclearly distinguish hyperspectral imaging from multispectral imaging,hyperspectral imaging generally captures a continual spectrum, with morespectral bands and higher spectral resolution, compared to multispectralimaging.

Multispectral and hyperspectral imaging techniques have been used in theprior art in medical imaging to inspect and diagnose diabetes, tumor,heart tissue ablation, fundus health and retinal diseases, etc, asdescribed in U.S. Pat. Nos. 6,937,885, 6,992,775, 8,224,425, 8,649,008,8,654,328, 9,002,085, 9,198,578, 9,204,805, 9,907,471, 9,968,285, etc,and they could also be used for other application such as biometricsensing, as disclosed in U.S. Pat. No. 7,394,919, etc.

However, there is still a need of a method and instrument to applyprinciples of multispectral and hyperspectral imaging to meibomianglands for dry eye diagnosis, with both visible and infrared spectra.Multispectral and hyperspectral meibography could bridge a significantgap between meibomian glands morphology and key palpebral tissueinformation such as vascular distribution, etc. Further, theintroduction of polarization control to meibography could minimizesuperficial surface reflectance and provide more information fordiagnosis.

SUMMARY OF THE INVENTION

It is an object of this invention to combine imaging with spectroscopyto extend the scope of meibography. Integration of spatial and spectralinformation enables a comprehensive analysis of meibomian glands.

It is another object of this invention to not only use infrared light,but simultaneously use visible light as well in meibography.

It is another object of this invention to provide a model of the eyelidas a multilayer structure with characterization parameters of each layerto investigate its absorption, reflection, transmission and scatteringproperties.

It is another object of this invention to provide a hyperspectralmeibography system to inspect core components concentrations indifferent layers of the eyelid, such as the melanin, collagen, lipidsconcentrations in conjunctival epithelium, meibomian glands, etc.

It is another object of this invention to use machine learning methods,such as support vector machines and artificial neural networks, toestimate characterization parameters of the eyelid, and evaluate theeyelid health based on both spatial and spectral information.

It is also an object of this invention to introduce polarization controlto meibography.

It is yet another object of this invention to present theinterconnection of the palpebral vasculature and meibomian glands byimage fusion with the vascular distribution in the visible spectrum andthe meibomian gland distribution in the infrared spectrum.

It is still another object of this invention to provide a handheldmeibographer to conveniently inspect the meibomian gland morphology andthe eyelid margin health.

The present invention relates to a method performed by a multispectralor hyperspectral meibomian gland imaging device, and the methodcomprises directing light from a broadband illuminator toward at least aportion of an everted eyelid of a subject, and the light covers visibleand infrared spectra; forming images of the everted eyelid using animaging system; recording the images using a detection system, whichseparates the images into a plurality of spectral channels with opticalfilters or a dispersive optical element, and the plurality of spectralchannels comprise at least one visible spectral channel and at least oneinfrared spectral channel; displaying the recorded images to adjust therelative position of the subject to optimize the image quality;digitally processing the recorded images to obtain spatial and spectralinformation; evaluating health of at least one meibomian gland of theeyelid by analyzing the spatial and spectral information. And thedigital processing further comprises image preprocessing, which includesreflectance calibration, transmittance calibration, image smoothing,contrast enhancement, and removal of illumination nonuniformity; featureextraction, in which the original data dimensionality is reduced and themost important subset of the original data is obtained to extractrelevant feature information of the everted eyelid; segmentation, inwhich a region of interest (ROI) of meibomian glands is designated andseparated in the images; parameter estimation, in which characterizationparameters of the everted eyelid are estimated; and evaluation, in whichpathological attributes and conditions are evaluated, including apercentage of meibomian gland coverage within the ROI, the meibomiangland atrophy, meibomian gland thickness and tortuosity.

The invention also discloses a multispectral or hyperspectral meibomiangland imaging device, which comprises a broadband illuminator to directlight toward at least a portion of an everted eyelid of a subject, andthe light covers visible and infrared spectra; an imaging system to formimages of the everted eyelid; and a detection system, which separatesthe images into a plurality of spectral channels with optical filters ora dispersive optical element, and the plurality of spectral channelscomprise at least one visible spectral channel and at least one infraredspectral channel.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates the layout of one embodiment of a multispectralmeibomian gland imaging device with direct illumination.

FIG. 2 presents the anatomical structures of an eye.

FIG. 3 shows four different embodiments of the detection system.

FIG. 4 illustrates one embodiment of a multispectral meibomian glandimaging device with transillumination.

FIG. 5 presents the detailed structure of one embodiment of thetransillumination illuminator.

FIG. 6 presents one embodiment of a hyperspectral meibography devicewith a transmissive spectrometer system.

FIG. 7 presents another embodiment of the hyperspectral meibographydevice with a reflective spectrometer system.

FIG. 8 presents one embodiment of image fusion of an upper eyelid bymultispectral and hyperspectral meibography.

FIG. 9 presents a comparison of the images of a healthy eyelid margin,and an eyelid margin of a patient with meibomitis, which presentsincreased vascularity around meibomian gland orifices.

FIG. 10 presents a model of an eyelid as a multilayer structure.

FIG. 11 presents the multiple reflection analysis in a modifiedKubelka-Munk theory model of the eyelid.

FIG. 12 presents a flowchart of the method of multispectral andhyperspectral meibography.

FIG. 13 presents a flowchart of the digital processing steps ofmultispectral and hyperspectral meibography.

FIG. 14 presents one embodiment of the imaging system and the detectionsystem of multispectral and hyperspectral meibography.

FIG. 15 presents another embodiment of the imaging system and thedetection system of multispectral and hyperspectral meibography using adichroic beamsplitter.

FIG. 16 presents one embodiment of the imaging system and the detectionsystem of a handheld multispectral meibographer.

FIG. 17 presents one embodiment of the exterior of a handheldmeibographer.

DETAILED DESCRIPTION OF THE INVENTION

In the prior art, direct illumination or transillumation meibography aredone with infrared spectrum (most commonly with near infrared spectrum),and the resultant image is a black and white grayscale image. In thisinvention, meibography is done with broadband imaging, covering visibleand infrared spectra. Multispectral and hyperspectral meibographysystems combine imaging with spectroscopy to measure not only spatialbut also spectral properties of the eyelids.

FIG. 1 illustrates one preferred embodiment of a multispectralmeibography device 1 with direct illumination. A broadband illuminator 2directs light toward at least a portion of an everted eyelid 4 of an eye3. Preferably, the illuminator 2 comprises a broadband light-emittingdiode (LED) array 21, and a translucent optical diffusing structure 22to distribute the illuminator output light more uniformly. The LED array21 covers both visible and infrared spectra. One embodiment of theilluminator has an annular shape with a central opening to allow lightto pass through. Optionally, there is a polarizer 23 to polarize theillumination light. The embodiment of the polarizer 23 could be a linearpolarizer, a circular polarizer, or polarizers of other polarizationstates. Meibomian glands 5 are embedded within the eyelid 4. In order toreveal meibomian glands, typically, an everted eyelid is used inmeibography. As used herein, the term “everted eyelid” may be construedto mean an eyelid with partial or complete exposure of the palpebralconjunctival surface or the eyelid margin. Part of the reflected andback scattered light from the eyelid is collected by an imaging system11, with an optical axis 10. The imaging system 11 could have manydifferent embodiments, FIG. 1 presents one preferred embodiment of 11,comprising a positive lens group 12, a negative lens group 13, and apositive lens group 14. Optionally, there is a polarization analyzer 24in the imaging system 11. In FIG. 1, the optional analyzer 24 is placedin front of all the lens groups. Preferably, the polarization states ofthe polarizer 23 and the analyzer 24 are appropriately chosen in orderto minimize the surface reflection from the superficial layer of theeyelid, no matter whether the eyelid is everted or not. If the eyelid iseverted, glares specularly reflected from the palpebral conjunctivalsurface could be avoided with proper polarization states of 23 and 24.For example, if both 23 and 24 are of linear polarization, they could beof orthogonal polarization states, and form a cross-polarization pair.If both 23 and 24 are of circular polarization, they could be of thesame polarization states. In some embodiments, the analyzer 24 is ofvarying polarization states, and the embodiment of a varyingpolarization analyzer could be a rotating polarizer or a liquid crystaltunable filter, etc. After passing the imaging system 11, light reachesa detection system 15, and in one embodiment, the detection systemcomprises a dichroic beamsplitter 30, one visible detector 31 and oneinfrared detector 32. The dichroic beamsplitter 30 splits the light intoone visible branch and one infrared branch to match the detectors. Notethat the optional analyzer 24 could also be placed in the detectionsystem 15, instead of the imaging system 11.

In some embodiments, if the illuminator 2 directs light at a largeincident angle close to the Brewster's angle, which is about 54˜55° forthe conjunctiva, the reflected light will have a high degree ofpolarization even if the optional polarizer 23 is removed. An analyzer24 of appropriately chosen polarization state could still be used tominimize the eyelid surface reflection.

In some embodiments, the illuminator 2 could also comprise a tunablenarrow band filter or a dispersive optical element to adjust theillumination spectrum. The tunable narrow band filter could be narrowband filters mounted in a rotating wheel, or it could be a liquidcrystal tunable filter, etc. Different spectral bands alternatessequentially to control the illumination spectral output. The output ofthe illuminator 2 at any moment is of narrow band in these embodiments,but the entire spectral coverage is still broadband.

FIG. 2 presents the anatomical structures of the eye 3. Meibomian glands5 are embedded in the upper eyelid 4. Similarly, meibomian glands 7 areembedded in the lower eyelid 6. The eyelashes 8 are located at the lidmargin, and the base of eyelashes are slightly anterior to the meibomiangland orifices. A lacrimal gland 9 is located at the upper lateralregion of each orbit. Secretory ducts of the lacrimal gland directsaqueous tears to the ocular surface and forms the aqueous layer of thetear film. The bulbar conjunctiva 38 and the palpebral conjunctiva 39forms a conjunctival sac, which serves as a tear reservoir.

FIG. 3 presents four different embodiments of the detection system 15.FIG. 3(a) is a single multispectral detector 16, with pixels coveringboth visible and infrared spectra. For example, the detector 16 couldhave four different types of pixels: red (R), green (G), blue (B) andnear infrared (NIR) pixels. In FIG. 3(b), the detection system comprisesa rotating spectral filter wheel 17, which has different spectralfilters embedded inside. A motor 18 rotates around an axis 19, and thespectral filters in front of a detector 20 change in a cyclic order. Thedetector 20 is sensitive to a broad range of spectrum including visibleand infrared spectra. In one embodiment, the alternating filters in thefilter wheel 17 are R, G, B, NIR four types. In another embodiment, theR, G, B spectral filters are directly embedded in the detector 20, whilesome or all of the R, G, B spectral filters allow a portion of the NIRlight to pass through, and the alternating filters in the filter wheel17 are of visible (VIS) and NIR two types. In still another embodiment,more spectral filters with narrow transmission bands could be used toimage with more spectral channels. Furthermore, in a hyperspectralimaging setup, the filter wheel 17 could be replaced by a tunablefilter, which is not necessarily rotary, and the tunable filter may bean acousto-optic tunable filter, or a liquid crystal tunable filter,etc.

In FIG. 3(c), the detection system comprises a dichroic beamsplitter 30,one visible detector 31 and one infrared detector 32, as employed inFIG. 1. The dichroic beamsplitter 30 splits the light into one visiblebranch and one infrared branch. The visible detector 31 is responsive tothe visible branch, which could be a regular detector with RGB threecolor channels. The infrared detector 32 is responsive to the infraredbranch. In FIG. 3(d), the detection system comprises a first dichroicbeamsplitter 33, which splits light into a visible branch and aninfrared branch. A detector 35 is responsive to the visible branch. Asecond dichroic beamsplitter 34 further splits the infrared branch intotwo different infrared spectra: Infrared 1 (IR₁) and Infrared 2 (IR₂),and detectors 36 and 37 are responsive to IR₁ and IR₂, respectively. Forexample, in one embodiment, 35 is responsive to the visible branch,covering a wavelength range of 400 nm to 750 nm, with RGB three colorchannels; 36 is responsive to the first infrared branch IR₁, covering750 nm to 900 nm; 37 is responsive to the second infrared branch IR₂,covering 900 nm to 1100 nm.

The embodiments of the beamsplitter(s) in FIG. 3 include but are notlimited to a cube beamsplitter, a plate beamsplitter, a pelliclebeamsplitter, etc.

In some embodiments, the detection system 15 comprises detectors withpolarization image sensors, where the optional polarization analyzer 24is directly formed on chip. One preferred embodiment of the polarizationimage sensor to work in the visible and near infrared spectra is SonyIMX250MZR/MYR or Sony IMX253MZR/MYR sensor. The Sony polarization imagesensors have a polarization analyzer 24 with four different directionalpolarization states. If the analyzer 24 has more than one polarizationstates as in the polarization image sensors, at least one pair ofpolarization states of the polarizer 23 in the illuminator and theanalyzer 24 are chosen to minimize superficial surface reflection of aneyelid. By comparing and combining the images from different analyzersfor each frame, the polarization information could be also used toextract the tissue properties.

FIG. 4 illustrates one embodiment of the multispectral and hyperspectralmeibography device with transillumination. A broadband illuminator 2 isplaced behind a cutaneous side of an everted eyelid 6, and the eyelid 6is everted over the illuminator 2 to direct light through the meibomianglands 7. The illuminator 2 is connected to a handle 25 to be held by anoperator. Part of the transmitted and forward scattered light iscollected by an imaging system 11 and reaches a detection system 15.

FIG. 5 presents the detailed structure of one embodiment of thetransillumination illuminator device. FIG. 5(a) is a side view, and FIG.5(b) is a front view. In FIG. 5(a), batteries 26 provide electricalenergy for the illuminator 2. Alternatively, a power cord (not shown)could be used to supply the electrical energy. Light is emitted by abroadband source 27, and one preferred embodiment of 27 is an array ofvisible and infrared LEDs. A light guide 28 transfers the broadbandlight to the illuminator 2. Optionally, a beam shaping system (notshown) could be inserted between the light source 27 and light guide 28,or it could be integrated within the light guide 28 to control theoutput light distribution. The batteries 26, the broadband light source27, and the light guide 28 are all installed within the handle 25.Optionally, a polarizer 23 (not shown in FIG. 5) is wrapped around theilluminator 2. The optional polarizer 23 could also be placed betweenthe light source 27 and the light guide 28, if 28 is apolarization-maintaining light guide, which can preserve thepolarization state during light propagation.

FIG. 5(b) presents a preferred shape of the illuminator 2 fortransillumination, of which the meniscus shape conforms to the contourof an eyelid to facilitate the eversion of the eyelid over theilluminator.

Sometimes, a hyperspectral imaging system is needed for a comprehensivespectral measurement. Hyperspectral imaging systems combine the spatialinformation with rich spectral information, which could elucidatecomponent concentration information otherwise difficult to obtainnon-invasively.

FIG. 6 presents one embodiment of a hyperspectral meibography device. Abroadband illuminator 2 directs light onto an eyelid 6 of an eye 3, withmeibomian glands 7 embedded within the eyelid 6. Part of the reflectedlight and back scattered light from the eyelid is collected by animaging system 40, with an optical axis 10. After the imaging system 40,the detection system is a transmissive spectrometer system 41 in FIG. 6.In the spectrometer system 41, a rotary scanning mirror 42 guides lightthrough a slit 43. One preferred embodiment of the scanning mirror 42 isa rotating polygon scanner. Preferably, the slit 43 is located at anintermediate image plane of the preceding imaging system 40 to spatiallyfilter out a slice of the image of the eyelid 6, and maintain only onespatial dimension, in order to avoid overlap of dispersed beams from twospatial dimensions. A collimating lens group 44 turns the light from theslit 43 into collimated light before reaching a transmission grating 45.Preferably, 45 is a transmission grating, such as a volume phaseholographic (VPH) transmission grating, though it could also be adiffractive lens, a prism, a Bragg grating, or other similar dispersiveelements. The transmission grating 45 separates the collimated incomingbeam into output beams of different wavelengths at different outputangles, and the output beams from the grating 45 pass through a focusinglens group 46 and reach a detector 47. Each image frame of the detector47 contains one spatial dimension and one spectral dimension. Thedetector 47 could be a charge-coupled device (CCD), a complementarymetal-oxide-semiconductor (CMOS), or other functionally similar imagingrecording devices. Typically, the detector could be used to obtainhundreds of spectral channels. At any moment, the detection system formsan image of one slice of the object through the slit 43. By rotating thescanning mirror 42, the entire two-dimensional object could be imaged.

FIG. 7 presents another embodiment of the hyperspectral meibographydevice. The illuminator 2, the imaging system 40 and the rotary scanningmirror 42 could be similar to those in FIG. 6. The spectrometer system50 in FIG. 7 is a reflective spectrometer system, instead of atransmissive spectrometer system as in FIG. 6. The main advantage of thereflective spectrometer system is a potential broader spectral range,since a transmissive spectrometer system tends to suffer from chromaticaberrations induced by material dispersion. In FIG. 7, a Czerny-Turnerconfiguration is illustrated as one preferred embodiment of thespectrometer system 50. A slit 51 is preferably located at anintermediate image plane of the preceding imaging system 40 to spatiallyfilter the image of the eyelid 6 to retain only one spatial dimension.The input beam from the slit 51 is collimated by a collimating mirror52, before reaching a grating 53. Preferably, the grating 53 is areflective diffraction grating, though 53 could also be a holographicgrating or other dispersive optical element. The grating 53 disperseslight of different wavelengths into different directions, and the outputbeams of the grating reaches a focusing mirror 54, which directs lightonto a detector 55. The detector 55 could be a CCD or a CMOS, or otherrecording devices.

The spectrometer system 50 in FIG. 7 is shown as a Czerny-Turnerconfiguration, but other spectrometer configurations such as a Littrowmount, an Ebert-Fastie mount, etc could also be used. Further, thegrating in FIG. 7 could be ruled on a concave or convex surface, andcorrespondingly, an Eagle spectrometer, a Wadsworth spectrometer, aDyson spectrometer, or other similar configurations, could also beemployed.

Note that in some embodiments, the scanning mirror 42 in FIG. 6 and FIG.7 could also be other scanning system, such as a point-scanningwhiskbroom scanner. The slit 43 or 51 is preferably replaced by a slitor a pinhole of an appropriate size to match the point-scanningwhiskbroom scanner.

Furthermore, the spectrometer system could also be a Fourier transformspectrometer. The spectrum of the object is obtained from the Fouriertransform of interferograms. One embodiment of the Fourier transformspectrometer has the basic setup as a Michaelson interferometer, whichcontains one moving mirror and one stationary mirror. The optical pathdifference (OPD) is modulated by the displacement of the moving mirror,so that the detected signal varies with the moving mirror displacement.With an imaging Fourier transform spectrometer, a two-dimensionalinterferogram for each moving mirror position could be recorded, and theFourier transform of the interferogram variation of each pixel willreveal its spectral distribution. The spectral calculation for all thepixels in the field of view will generate a hypercube of the eyelid andadjacent tissues.

Moreover, spectral scanning methods which capture the two-dimensionalspatial image in a single frame, and step through the spectral rangeduring scanning could also be employed in multispectral andhyperspectral meibography. These spectral scanning methods could employspectral filters in the form of rotating narrow band spectral filters,acousto-optic tunable filters, and liquid crystal tunable filters, etc.These filters could be placed on either the illumination side or thedetection side of the meibography system.

Note that although in the preferred embodiments, the multispectral andhyperspectral meibography systems work in the visible and infraredspectra, these systems could also be readily extended to work in theultraviolet (UV) spectrum as well.

During a measurement, it's preferred to display recorded images in realtime to adjust the relative position of the subject, to ensure subjectcomfort and optimize the image quality.

It is also preferred to use a control system to control and coordinatethe broadband illuminator, the imaging system, and the detection system.

The multispectral and hyperspectral meibography systems disclosed inthis invention have at least two spectral channels, one in the visiblespectrum and the other in the infrared spectrum. However, amultispectral system could have two to twenty, or even more spectralchannels, and a hyperspectral system usually has hundreds or even morespectral channels.

Different wavelengths have different penetration depths. Near-infraredis generally used as a spectral window for deeper biological tissuemeasurement, since NIR spectrum penetrates deeper into the tissue thanvisible light (VIS), mid-infrared (MIR), and long wave infrared (LWIR).This is partly because some chromophores such as hemoglobin and melaninabsorb UV and VIS strongly, and water absorbs MIR and LWIR strongly. Thepenetration depth is about 1 mm below the tissue surface for the visiblelight, and about 1 to 5 mm below the tissue surface for NIR. Inmultispectral and hyperspectral meibography, NIR contains moreinformation about the deeper structures including the meibomian glands,while other wavelength bands, including the visible light, contain moreinformation of the structures immediately below the surface.

After being recorded by the detection system, the images are digitallyprocessed. In multispectral and hyperspectral meibography, digitalprocessing in general comprises image preprocessing 112, featureextraction 113, segmentation 114, parameter estimation 115, andevaluation 116—five main steps. The similarities and differences ofthese five steps for a multispectral system and a hyperspectral systemare explained in the following.

Image preprocessing 112 includes the steps to calibrate the irradiancereflectance with reference targets. This step is the same for bothmultispectral and hyperspectral imaging systems. Preferably, thecalibration is done with a white image and a dark image. The white imageis taken with a reference white panel, and preferably the referencewhite panel is a Spectralon white diffuse reflectance target. Theirradiance reflectance could be expressed as

$\begin{matrix}{\rho = {\frac{{DN_{s}} - {DN_{dark}}}{{DN_{white}} - {DN_{dark}}}\rho_{white}}} & (1)\end{matrix}$

where DN stands for data number, DN_(s) is the data number of the sampleat each pixel of each spectral channel of an image sensor, and inmeibography, the sample is usually part of an eyelid. DN_(dark) is thedata number of the dark current with the optical shutter completelyclosed. DN_(white) is the data number of the reference white panel.ρ_(white) is the irradiance reflectance of the reference white panel.For a Spectralon target, typically, ρ_(white)>99% in the spectral rangeof 400 nm to 1500 nm, and ρ_(white)>95% from 250 nm to 2500 nm.

If no reference white panel is available, the calibration could also bedone with other optical materials with known reflectance properties as

$\begin{matrix}{\rho = {\frac{{DN_{s}} - {DN_{dark}}}{{DN_{r}} - {DN_{dark}}}\rho_{r}}} & (2)\end{matrix}$

where ρ_(r) is the irradiance reflectance of the reference opticalmaterial, such as a BK7 glass reference plate, at the calibrationwavelength, and DN_(r) is the corresponding data number. The calibrationcan be repeated for a series of wavelengths, and a final curve of pcould be interpolated and extrapolated for the full wavelength range. Aneutral grey reference target could also be employed to simplify thecalibration process for different wavelengths.

The above reflectance calibration is mainly used for directionillumination. For transillumination, a similar procedure oftransmittance calibration could be done.

Image preprocessing further includes image smoothing, contrastenhancement, and removal of illumination nonuniformity, etc. Imagesmoothing could be done using median filters of a properly chosen sizeor other smoothing algorithms Contrast enhancement, or contrast stretch,includes multiple transform methods in both spatial domain and spectraldomain. Spatial contrast enhancement methods include stretching theoriginal data number range to fill the full gray level range, withlinear, nonlinear, histogram equalization, reference stretch and otherrelated methods. Spectral contrast enhancement methods includenormalization stretch, spatial domain blending, etc. Removal of theillumination nonuniformity could be partly done with the reflectance ortransmittance calibration, and partly done with a high-pass filter or ahomomorphic filter.

The second step of digital processing is feature extraction 113. Featureextraction aims to reduce the original data dimensionality and obtainthe most important subset of the original data to extract relevantfeature information of the eyelid. Similar procedure could be applied toboth multispectral and hyperspectral meibography, although it'sespecially useful for hyperspectral meibography due to its high spectraldimensionality. Feature extraction is not an absolutely necessary stepfor some analysis applications, but it is preferred for multispectraland hyperspectral meibography. Commonly used feature extraction methodsinclude principal component analysis (PCA), minimum noise fraction(MNF), independent component analysis (ICA), spectral angle mapper (SAM)and spectral information divergence (SID), etc.

In the feature extraction step, other than the raw spectral channelsphysically located in the detection system, some of these raw spectralchannels could be combined and manipulated to generate additional“digital spectral channels” to further improve feature contrast. Forexample, with principal component analysis (PCA), new digital channels,i.e. the principal components, could be constructed as weighted linearcombinations of the raw spectral channels.

As another example, additional digital spectral channels could begenerated based on two indices. The first index is the “DifferenceMeibomian Gland Index” (DMGI), for a multispectral meibography devicewith direct illumination,

DMGI=|R _(r) −R _(n)|  (3)

where R_(r) and R_(n) are the irradiance reflectance values of red andnear infrared spectral channels calculated at each pixel. The irradiancereflectance includes light of both reflection and backward scatteringthat is collected by the device.

Similarly, for a multispectral meibography device withtransillumination,

DMGI=|T _(r) −T _(n)|  (4)

where T_(r) and T_(n) are the irradiance transmittance values of red andnear infrared spectral channels calculated at each pixel. The irradiancetransmittance includes light of both transmission and forward scatteringthat is collected by the device.

The second index is the “Normalized Difference Meibomian Gland Index”(NDMGI), and for direct illumination,

NDMGI=|(R _(r) −R _(n))/(R _(r) +R _(n))|  (5)

Similarly, for transillumination,

NDMGI=|(T _(r) −T _(n))/(T _(r) +T _(n))|  (6)

These indices are evaluated at each pixel, so an entire frame of ameibomian gland image can be analyzed to potentially enhance thecontrast of the meibomian glands with respect to neighboring tissues.Thus each frame based on either index could be considered as anadditional digital spectral channel. Other indices containing visibleand near infrared spectra information could also be used to increase thecontrast and generate additional digital spectral channels.

If a hyperspectral imaging setup is used, the DMGI and NDMGI indicescould be evaluated at narrower, more specific spectral bands.

Different spectral channels emphasize different features in clinicallyobtained multispectral and hyperspectral images of the eyelid, no matterwhether they are the raw spectral channels or the additional digitalspectral channels. Experimental results demonstrate that infraredspectral channels tend to have relatively high image contrast of themeibomian glands, which is suitable to study the meibomian glandsdistribution and morphology. Visible spectral channels have low imagecontrast of healthy meibomian glands, and this might be the primaryreason that meibography was most commonly done in the infrared spectrumin the prior art. But visible green and blue color channels tend to havehigh contrast of the blood vessels with adjacent tissues, and usuallythe green color channel tends to have the highest image contrast ofvasculature. The visible red color channel tends to have low contrastand the red color channel data numbers of an image of an everted eyelidtend to be much higher than the green and blue channels, due to strongerreflectance by the blood vessels and the collagen fibers in the redcolor channel

However, clinical studies demonstrate that in some patients withmeibomian gland dysfunction (MGD), the dilated central duct filled withexcess meibum could enhance the contrast of the meibomian glands withadjacent tissues, even in the visible spectrum. The increased meibomiangland contrast for MGD patients is especially prominent close to the lidmargin. Further, when there is meibomian gland obstruction at the glandorifice, the image contrast at the visible blue and green color channelstend to increase at the orifices. Hence, although the meibomian glandimage contrast tends to be low in the visible spectral channels, visiblespectral images might contain critical pathological information todistinguish a MGD patient from a normal subject.

Multispectral and hyperspectral imaging enables image fusion to revealmore biological features. For example, image fusion with at least onevisible spectral channel and at least one infrared spectral channelcould be used to assess the interconnection of palpebral vasculature andmeibomian glands, with visible channels to present the blood vessels andinfrared channels to present the meibomian glands. For example, a pairof (G, IR) two grayscale images could be fused.

Further, any three different spectral channels, whether they are the rawspectral channels or additional digital spectral channels, could befused to generate false color images, which would help to visualize themultispectral and hyperspectral images. For example, the visible redchannel, and two infrared spectral channels could be groups as (R, IR₁,IR₂) to replace conventional (R, G, B) three color channels in an imageto generate a false color image. Each of the (R, IR₁, IR₂) channel is atwo-dimensional gray scale image. Similarly, three infrared spectralchannels (IR₁, IR₂, IR₃) could be used to generate an infrared-onlyfalse color images of meibomian glands. If false color images to overlaythe vasculature with the meibomian glands is preferred, (G, IR₁, IR₂),(B, IR₁, IR₂), or (G, B, IR₂) could be used.

Additional digital spectral channels could also be used in the falsecolor images. For example, if the detection system only has red (R) andinfrared (IR) two spectral channels, a third digital spectral channel ofR−IR, or (R−IR)/(R+IR) could be generated, and (R, IR, R−IR) or [R, IR,(R−IR)/(R+IR)] could be used in the false color images. As anotherexample, if the detection system has RGB three visible channels, andIR₁, IR₂ two infrared channels, any three channels out of the availableraw and additional digital spectral channels could be combined togenerate false color images, such as [G, IR₂, (R−IR₁)/(R+IR₁)], [IR₁,IR₂, (IR₁−IR₂)/(IR₁+IR₂)], [B, IR₁, (IR₁−IR₂)/(IR₁+IR₂)], etc.

Hence, feature extraction 113 comprises (108) generating additionaldigital spectral channels, by combination of at least two of a pluralityof spectral channels of the detection system; (109) generating falsecolor images of the everted eyelid by selecting and combining spectralchannels out of a plurality of spectral channels of the detection systemand the additional digital spectral channels. Typically, three spectralchannels are selected for a false color image.

FIG. 8 presents one embodiment of image fusion of an upper eyelid by thedisclosed multispectral and hyperspectral meibography. At least oneinfrared spectral channel is used for imaging of the meibomian glands asshown in FIG. 8(a). At least one visible spectral channel, preferablyvisible green channel, is used for imaging of the palpebral vasculardistribution as shown in FIG. 8(b). FIG. 8(c) is a fused image to showboth physiological features.

Image fusion is especially helpful for the diagnosis of meibomitis,since there could be increased vascularity of the eyelid margin,invading the orifices of meibomian glands in some patients. FIG. 9presents part of an image of the eyelid margin to emphasize meibomiangland orifices. FIG. 9(a) presents a healthy eyelid margin, where thepalpebral vasculature close to the eyelashes 75 and the meibomian glandorifices 76 is barely visible. FIG. 9(b) presents the eyelid margin of apatient with meibomitis, a subgroup of MGD, where the palpebral bloodvessels 77 close to the meibomian gland orifices are clearly recorded bythe visible spectral channels. The infrared spectral channels canfurther help to locate the meibomian gland orifices.

Note that direct illumination is preferred, if both the palpebralvasculature and the meibomian gland distribution are to be studiedsimultaneously. If transillumination is used, the entire thickness of aneyelid will be illuminated through, and the scattering of all differentlayers of blood vessels and adjacent tissues inside the eyelid willgenerate a relatively uniform background, and largely wash out thevascular distribution information in the visible channels, and theresultant transillumination image is mostly of meibomian glands only. Inthe visible spectrum, the resultant transillumination image is a lowcontrast image of slightly dark meibomian glands on a red background.

After feature extraction, segmentation 114 is the next step to designateand separate the region of interest (ROI) from the remaining parts ofthe images. In meibography, segmentation is the step to isolate aportion of an image that correspond to the meibomian glands. Typically,ROI is a continuous region including the spaces between adjacentmeibomian glands. The remaining parts of the image, such the eyelashes,facial skin, the ocular surface, etc are masked out. In bothmultispectral and hyperspectral systems, segmentation can be carried outbased on spectral and spatial analysis or feature recognition.Segmentation could be also be done with methods such as support vectormachines (SVM) and artificial neural networks (ANN), etc., to classifyROI and non-ROI areas.

Once the region of interest of the meibomian glands is selected aftersegmentation, the next step is parameter estimation 115, wherecharacterization parameters are estimated. Due to the availability ofspectral channels, usually hyperspectral systems can analyze andestimate a lot more parameters than multispectral systems. Formultispectral meibography, a basic characterization parameter could be aBoolean parameter of 0 or 1 to distinguish image pixels representingpart of a meibomian gland and non-meibomian gland pixels within the ROI,hence the meibomian gland morphology could be obtained for diagnosis.For hyperspectral meibography, this step could generate estimation ofdetailed characterization parameters of different structures of theeyelid. These key characterization parameters include melanin, collagenand lipids concentration, blood vessel oxygenation, etc. Further,meibomian gland distribution and vascular distribution could be mappedbased on these characterization parameters. Supervised or unsupervisedpixel and subpixel based methods could be used for hyperspectralanalysis. A number of regression and classification methods based onparametric methods or non-parametric machine learning methods such assupport vector machines (SVM) and artificial neural networks (ANN), etc.could be used for meibography.

The last step of digital processing is evaluation 116. This step isapplied to both multispectral and hyperspectral systems. Pathologicalattributes and conditions, such as the percentage of meibomian glandcoverage within the

ROI, the meibomian gland atrophy, meibomian gland thickness andtortuosity, etc., could be evaluated to generate a diagnostic result ofthe meibomian gland health. Further, in a hyperspectral meibographysystem, more parameters, such as blood oxygenation, lipids and collagenconcentration, etc, could enable comprehensive evaluation of themeibomian glands and adjacent structures.

The entire digital processing of the five steps 112-116 could beautomated, but the final evaluation and diagnosis could be done bymedical professionals, if preferred. The evaluation of meibomian glandshealth comprises (110) assessing an interconnection of palpebralvasculature and at least one meibomian gland by image fusion with atleast one visible spectral channel and at least one infrared spectralchannel; (111) evaluating meibomian gland orifice obstruction andpalpebral vasculature of the everted eyelid with images of an eyelidmargin.

In the following, a preferred meibography parameter estimation method isdisclosed in detail, which includes a forward model and an inversemodel. The forward model is based on the optical reflection,transmission, scattering and absorption properties of the eyelidphysiological structures to predict the signal output of the detectionsystem; and the inverse model is based on a parametric method or amachine learning method to estimate eyelid characterization parametersfrom detected signals. Those skilled in the art could readily extendother similar procedures to meibography.

In the forward model, the everted eyelid is molded as a multilayerstructure as shown in FIG. 10. Note that the positional words “anterior”and “posterior”, and prefixes “pre-” and “post-” are the opposite ofnormal use to be consistent with the anatomical terminology since theeyelid is everted. The layers of the eyelid are labeled from theconjunctival side above to the cutaneous side below in sequence: theconjunctival epithelium 60, the conjunctival stroma 61, the tarsal plate62, the fascia or aponeurosis 69, the orbicularis oculi muscle 70, thehypodermis 71 of the eyelid, the dermis 72 of the eyelid, the livingepidermis 73 of the eyelid, and the stratum corneum 74 of the epidermisof the eyelid. 69 is the fascia of the lower eyelid retractor or thelevator aponeurosis of an upper eyelid. For the area of the evertedeyelid that contains a meibomian gland, the tarsal plate could befurther divided into the post-meibomian gland tarsal plate 63, themeibomian gland 64, and the pre-meibomian gland tarsal plate 68. Thepost-meibomian gland tarsal plate 63 includes the loose connectivetissue. In the preferred method, the meibomian gland 64 is furtherdivided into three sublayers, the posterior meibomian gland cells 65,the lipids or meibum 66, the anterior meibomian gland cells 67. The skinof an eyelid including 72-74 is about 0.5˜1 mm thick, and the totalthickness of a healthy eyelid is about 4˜5 mm. The total thickness of aneyelid could vary significantly with pathological conditions, such as achalazion, or a sty, etc.

The above division of the layers and sublayers is meant to represent thecore structures of the eyelid for optical modeling purposes, hence it isnot an exact anatomical representation. If necessary, each layer can bedivided further into sublayers, and additional anatomical layers couldbe added. For example, the living epidermis 73 of an eyelid could befurther divided into four sublayers: stratum basale, stratum spinosum,stratum granulosum, and stratum lucidum, from the dermis side to thestratum corneum side.

For direct illumination on an everted eyelid, the layers beneath thetarsal plate 62, i.e. layers 69-74, contribute minimally to thereflected and back scatted images, and could be ignored, if the inducederrors are acceptable. For transillumination, all the layers andsublayers are analyzed, though some structures may be simplified. Fromthe perspective of computing efficiency and robustness, a system with asmall number of parameters is preferred, hence direct illumination ispreferred for hyperspectral meibography.

In the forward model, an input vector space containing biologicalcharacterization parameters of the eyelid tissues, is mapped to anoutput vector space of the spectral components of the hyperspectralmeibography system,

p→x, x=f(p)   (7)

f is the forward mapping function, p is an input vector of eyelidcharacterization parameters, and p=[p_(th0), p_(th1), p_(th2), p_(col),p_(mel), p_(car), p_(bil), p_(myo), p_(lip), p_(w), p_(bld), p_(o)], andeach component of the vector p is a scalar to characterize a certainfeature of the eyelid, i.e. any k-th component p_(k) ∈

¹. p_(th0) is the post-meibomian gland (post-MG) tarsal plate thickness,p_(th1) is the meibomian gland layer thickness, p_(th2) is the pre-MGtarsal plate thickness, p_(col) is the collagen concentration in alayer, p_(mel) is the melanin concentration, p_(car) is the β-caroteneconcentration, p_(bil) is the bilirubin concentration, p_(myo) is themyosin concentration, p_(lip) is the lipids concentration, p_(w) is thewater volume fraction, p_(bld) is the blood volume fraction, p_(o) isthe oxygenation saturation. Preferably, a series of the thicknesses oflayers, and core components concentrations in each layer are modeled toform the input vector p. x is the calibrated irradiance reflectance orthe preprocessed data number of each spectral channel at a spatialpoint. x=[x(λ₁), x(λ₂), . . . , x(λ_(M−1)), x(λ_(M))], and the subscript“M” is the total number of multispectral or hyperspectral channels, andx ∈

^(M).

The myosin concentration is mainly determined by the distribution of themuscle of Riolan and the rest of the orbicularis oculi muscle, and thelipid concentration is associated with the meibomian glands. Otherrelevant parameters, such as the concentration of elastin, actin,mucins, and other chromophores, could also be included in the model, andpotentially could be more accurate, but on the other hand, the analysiswith more parameters might be more complicated, noisier and moretime-consuming. It's also preferred that the total number of parametersof all layers in the characterization vector p is smaller than the totalnumber of spectral channels in the multispectral or hyperspectralimaging system, in order to obtain robust regression. With thistrade-off in mind, the choice of the forward model is aimed to quantifymeibomian glands morphology and vascular distribution in the acquiredhyperspectral images with core components.

The forward mapping p→x could be analyzed with a deterministic orstochastic model. The deterministic model could be a diffusion theorymodel, a radiative transfer model or a Kubelka-Munk (KM) theory model,and the most common stochastic model is a Monte-Carlo model. As anexample, a modified KM theory model is described herein, those skilledin the art could readily extend the analysis to other types of models.

The KM theory describes the resultant light from turbid materials withthe irradiance transmittance (t_(n)) and reflectance (r_(n)) at eachlayer. Each layer is modeled as a homogenous layer of a material with acertain thickness that absorbs and scatters incident light. Theirradiance transmittance t_(n)(λ) and reflectance r_(n)(λ) of the n-thlayer are:

$\begin{matrix}{{t_{n}(\lambda)} = \frac{4{\beta_{n}(\lambda)}}{{\lbrack {1 + {\beta_{n}(\lambda)}} \rbrack^{2}e^{{K_{n}{(\lambda)}}d_{n}}} - {\lbrack {1 - {\beta_{n}(\lambda)}} \rbrack^{2}e^{{- {K_{n}{(\lambda)}}}d_{n}}}}} & (8) \\{{r_{n}(\lambda)} = \frac{\lbrack {1 - {\beta_{n}(\lambda)}^{2}} \rbrack \lbrack {e^{{K_{n}{(\lambda)}}d_{n}} - e^{{- {K_{n}{(\lambda)}}}d_{n}}} \rbrack}{{\lbrack {1 + {\beta_{n}(\lambda)}} \rbrack^{2}e^{{K_{n}{(\lambda)}}d_{n}}} - {\lbrack {1 - {\beta_{n}(\lambda)}} \rbrack^{2}e^{{- {K_{n}{(\lambda)}}}d_{n}}}}} & (9)\end{matrix}$

where d_(n) is the thickness of the n-th layer, and β_(n)(λ) andK_(n)(λ) are

β_(n)(λ)=√{square root over (A _(n)(λ)/[A _(n)(λ)+2S _(n)(λ)])}  (10)

K _(n)(λ)=√{square root over (A _(n)(λ)[A _(n)(λ)+2S _(n)(λ)])}  (11)

Further, the coefficients A_(n)(λ) and S_(n)(λ) are determined by theabsorption coefficient a_(n)(λ) and the reduced scattering coefficients_(n)′(λ) of each layer, and the final relations are dependent on thespecific scattering and absorption properties of a material. In onepreferred method,

$\begin{matrix}{{A_{n}(\lambda)} = \frac{a_{n}(\lambda)}{\frac{1}{2} + {\frac{1}{4}\lbrack {1 - \frac{s_{n}^{\prime}(\lambda)}{{s_{n}^{\prime}(\lambda)} + {a_{n}(\lambda)}}} \rbrack}}} & (12) \\{{S_{n}(\lambda)} = \frac{s_{n}^{\prime}(\lambda)}{\frac{4}{3} + {\frac{38}{45}\lbrack {1 - \frac{s_{n}^{\prime}(\lambda)}{{s_{n}^{\prime}(\lambda)} + {a_{n}(\lambda)}}} \rbrack}}} & (13)\end{matrix}$

Specifically, the absorption coefficient is dependent upon thechromophore types and concentrations in a layer. For example, in the toplayer of an everted eyelid, the conjunctival epithelium, the absorptioncoefficient is modeled as

a ₁(λ)=p _(col_1) a _(col)(λ)+p _(mel_1) a _(mel)(λ)+p _(w_1) a_(w)(λ)+p _(car_1) a _(car)(λ)   (14)

where a_(col)(λ), a_(mel)(λ), a_(w)(λ), and a_(car)(λ) are the specificabsorption coefficients of collagen, melanin, water, and β-carotene.

The absorption coefficient of the conjunctival stroma could be modeledas

a ₂(λ)=p _(col_2) a _(col)(λ)+p _(bld_2)[p _(o_2) a _(ohb)(λ)+(1−p_(o_2))a _(dhb)(λ)]+(p _(w_2)+0.9p _(bld_2))a _(w)(λ)+p _(car_2) a_(car)(λ)+p _(bil_2) a _(bil)(λ)   (15)

where a_(ohb)(λ), a_(dhb)(λ), and a_(bil)(λ) are the specific absorptioncoefficients of oxygenated hemoglobin, deoxygenated hemoglobin, andbilirubin. It also implicitly uses the fact that water is about 90% ofthe blood.

As another example, the absorption coefficient of the pre-MG or post-MGtarsal plate could be modeled as a k-th layer with

a _(k)(λ)=p _(col_k) a _(col)(λ)+p _(bld_k)[p _(o_k) a _(ohb)(λ)+(1−p_(o_k))a _(dhb)(λ)]+(p _(w_k)+0.9p _(bld_k))a _(w)(λ)+p _(car_k) a_(car)(λ)+p _(bil_k) a _(bil)(λ)+p _(myo_k) a _(myo)(λ)   (16)

where a_(myo)(λ) is the specific absorption coefficient of myosin, whichis contained in the muscle of Riolan surrounding the meibomian glands.

Similar absorption coefficients could be derived for all the otherlayers with different chromophore distributions, and in general,

$\begin{matrix}{{a_{n}(\lambda)} = {\sum\limits_{ch}{p_{ch_{-}n}{a_{ch}(\lambda)}}}} & (17)\end{matrix}$

where p_(ch_n) is the concentration of a type of chromophore in the n-thlayer, and a_(ch)(λ) is the specific absorption coefficient of thatchromophore.

The reduced scattering coefficient of each layer is modeled with theform of

s′ _(n)(λ)=C _(n)λ^(−D) ^(n)   (18)

where the parameters C_(n) and D_(n) are determined experimentally oradapted from the literature. Sometimes, C_(n) is simplified to belinearly proportional to the collagen concentration p_(col) of the n-thlayer.

In the original Kubelka-Munk theory, surface reflection of the turbidmaterial is neglected. In meibography and a lot of other applications,surface reflection has to be added into the model. At the interface ofdifferent layers, Fresnel equations could be used to model theirradiance transmittance and reflectance. In many embodiments, thedistance of the eyelid to the imaging system of the meibography deviceis much longer than the length of a meibomian gland, hence theillumination could be approximated as normal incidence, and simplifiedFresnel equations could be used. The irradiance transmittance τ_(n)(λ)and reflectance ρ_(n)(λ) at the interface with normal incidence are

$\begin{matrix}{{\tau_{n}(\lambda)} = \frac{4{N_{n}(\lambda)}^{2}}{\lbrack {{N_{n + 1}(\lambda)} + {N_{n}(\lambda)}} \rbrack^{2}}} & (19) \\{{\rho_{n}(\lambda)} = \lbrack \frac{{N_{n + 1}(\lambda)} - {N_{n}(\lambda)}}{{N_{n + 1}(\lambda)} + {N_{n}(\lambda)}} \rbrack^{2}} & (20)\end{matrix}$

where N_(n)(λ) and N_(n+1)(λ) are the refractive indices of the incidentand transmitting layer.

If the incident angle is not negligible, the complete Fresnel equationscould be used to derive irradiance transmittance and reflectance foroblique incidence.

In the modified KM theory model in this invention, multiple reflectionsbetween two adjacent layers and interfaces are taken into account. Asshown in FIG. 11, after the calculation of the transmittance andreflectance of each layer with the KM theory, the net effect of the n-thlayer could be modeled as if the transmission and reflection happen at asingle plane, the n-th inner plane. Because of the material of eachlayer is turbid or translucent, the reflected light will addincoherently, thus the phase factors can be dropped and the exactlocation of the inner plane doesn't matter, as long as it's within thethickness of each layer. With this novel abstraction, each layer can besimplified as an optical plane analogous to an optical interface. InFIG. 11(a), the first several layers are plotted to represent thissimplified model. Each layer is abstracted as an inner plane with twoboundary interfaces. The interface 0, inner plane 1, interface 1, innerplane 2, and interface 2 are labeled as 80 to 84. The irradiancetransmittance and reflectance of each interface are denoted as τ and ρfor an optical interface, and as t and r for an abstracted inner plane.

In FIG. 11(b), detailed calculation of multiple reflections between theinterface 0 and the inner plane 1 are plotted, and the sum of thereflectance and transmittance of these two layers are:

$\begin{matrix}{R_{i,1} = {{\rho_{0} + {\tau_{0}^{2}r_{1}{\sum\limits_{m = 0}^{\infty}( {r_{1}\rho_{0}} )^{m}}}} = {\rho_{0} + \frac{\tau_{0}^{2}r_{1}}{1 - {r_{1}\rho_{0}}}}}} & (21) \\{T_{i,1} = {{\tau_{0}t_{1}{\sum\limits_{m = 0}^{\infty}( {r_{1}\rho_{0}} )^{m}}} = \frac{\tau_{0}t_{1}}{1 - {r_{1}\rho_{0}}}}} & (22)\end{matrix}$

where the subscript “i” stands for an inner plane. The net effect ofthese two layers could be treated as a pair of new transmittance andreflectance (T_(i,1), R_(i,1)) at the inner plane 1, and the next twosurfaces could be analyzed. This whole calculation could be repeated inan iterative process.

FIG. 11(c) presents a general case of interface n 85, i.e. the n-thinterface, and inner plane n+1 86, i.e. the (n+1)-th inner plane. Thetransmittance and reflectance of the interface n itself are (ρ_(n),ρ_(n)). However, taking all the layers above the n-th interface intoaccount, the net transmittance and reflectance are (T_(n), R_(n)) at theposition of the n-th interface. The multiple reflections between theinterface n and the inner plane n+1 further generate

$\begin{matrix}{R_{i,{n + 1}} = {R_{n} + \frac{T_{n}^{2}r_{n + 1}}{1 - {r_{n + 1}R_{n}}}}} & (23) \\{T_{i,{n + 1}} = \frac{T_{n}t_{n + 1}}{1 - {r_{n + 1}R_{n}}}} & (24)\end{matrix}$

Similarly, FIG. 11(d) illustrates the general case with an interface n+187, i.e. the (n+1)-th interface beneath the (n+1)-th inner plane 86. Thetransmittance and reflectance of the (n+1)-th inner plane itself are(t_(n+1), r_(n+1)). However, taking all the layers above the (n+1)-thinner plane into account, the net transmittance and reflectance are(T_(i,n+1), R_(i,n+1)) at the position of the (n+1)-th inner plane. Themultiple reflections leads to

$\begin{matrix}{R_{n + 1} = {R_{i,{n + 1}} + \frac{T_{i,{n + 1}}^{2}\rho_{n + 1}}{1 - {\rho_{n + 1}R_{{in} + 1}}}}} & (25) \\{T_{n + 1} = \frac{T_{i,{n + 1}}\tau_{n + 1}}{1 - {\rho_{n + 1}R_{i,{n + 1}}}}} & (26)\end{matrix}$

The above iteration in FIGS. 11(c) and (d) could be repeated untilreaching the last interface or the last layer of infinite thickness in amodel. The irradiance transmittance and reflectance of the entirestructure are hence analyzed.

Sometimes, significant simplification of the forward model with minimalparameters is employed, if a quick and coarse evaluation of the eyelidand meibomian glands is enough. For example, the everted eyelid could bemodeled as a three-layer structure of a layer of top tissue, a layer oflipids, and a layer of bottom tissue, where the middle lipid layer is ofvariable thickness, and the bottom tissue layer can be modeled assemi-infinite for direct illumination. Further, in one even moresimplified model, the everted eyelid is modeled as a single layer withdifferent concentration of core components, such as hemoglobin,collagen, and lipids, etc.

If the optional polarizer 23 and analyzer 24 are used in the setup, theycould be of properly chosen polarization states such that the surfacereflection at the superficial layer is minimized Polarization control isespecially important, if a simplified eyelid model is used. For example,if the incident illumination is of horizontally polarized light, aftersurface reflection, the surface reflected light is mostly ofhorizontally polarized light. If the analyzer is a vertical analyzer,most of the surface reflected light will be blocked. As another example,if the incident illumination is of left hand circularly polarized light,the surface reflected light is mostly of right hand circularly polarizedwith a reversed helicity. If the analyzer is a left hand circularanalyzer, most of the surface reflected light will be blocked. Lighttravels deeper into the tissue and scattered back after multiplescattering are of a low degree of polarization, hence part of thescattered light will be able to pass through the analyzer. Therefore,with polarization control, the surface reflection could be minimized andthe deeper tissue structure, including the meibomian glands could berevealed. With polarizer 23 and analyzer 24 in place, the Fresnelequations of transmittance and reflectance, and the calculation ofmultiple reflections should take polarization into account, especiallyfor oblique incidence. The depolarization of each layer related to thescattering coefficients could be estimated, and the forward model couldbe modified accordingly.

In the inverse model, the multispectral or hyperspectral data is used toestimate the characterization parameters of the meibomian glands andadjacent eyelid tissues:

x→p   (27)

The inverse model could be parametric or non-parametric. If a parametricmethod of the inverse model is used, the inverse process of the forwardmodel is used to recovery the characterization parameters. The parameterestimation based on the inverse function f⁻¹ of the forward model in Eq.(7) is usually difficult to obtain analytically due to thenonlinearities in the forward model. However, the parameter estimationcould be obtained numerically, if the previous forward mapping functionf of different combination of parameters is unique. Numerical parameterestimations are obtained after iterations starting from a guess solutionwith a numerical method such as Newton-Raphson method, Broyden's method,bisection method, etc. The guess solution is randomly chosen within apreset parameter range based on a priori knowledge of the eyelid tissueproperties. Usually the Jacobian matrix or other similar derivativebased matrices are used in the iteration process. Sometimes, an inversemodel based on least-square minimization, maximum likelihood, orindependent component analysis, or other methods could be used forparameter estimation of simplified eyelid models.

If non-parametric methods are used in the inverse model, the eyelidcharacterization parameters could be estimated with a machine learningmethod. The machine learning method could be supervised learning,semi-supervised learning, and unsupervised learning. In the following, asupport vector machine based regression method as a representativenon-parametric, supervised machine learning method for the inverse modelis described.

The input training data points are {(x₁, p₁), (x₂, p₂), . . . ,(x_(l−1), p_(l−1)), x_(l), p_(l))}, where x_(i) ∈

^(M), p_(i) ∈

¹, and the subscript “l” denotes the number of available training datapairs. The training data could be generated with the aforementionedforward model, or collected from experiments. The goal of the inversemodel is to find a function g(x) to estimate each scalar component p_(i)of the target vector p with a maximum deviation of ε:

g(x)=

w, x

+b   (28)

Hence given a spectral response vector x_(i) at a given spatial point,g(x_(i)) is used to estimate p_(i). However, the ε-precision might notbe a feasible constraint sometimes, and slack variables ξ_(i) and ξ*_(i)could be introduced to relax the error precision ε to allow some moreerrors of potential outliers, with a so-called “soft margin”, and theconstrained optimization problem has the formulation:

$\begin{matrix}{{{{minimize}\mspace{14mu} \frac{1}{2}{w}^{2}} + {C{\sum\limits_{i = 1}^{l}( {\xi_{i} + \xi_{i}^{*}} )}}}{subject}\mspace{14mu} {to}\mspace{14mu} \{ {\begin{matrix}{{p_{i} - {\langle{w,x_{i}}\rangle} - b} \leq {ɛ + \xi_{i}}} \\{{{\langle{w,x_{i}}\rangle} + b - p_{i}} \leq {ɛ + \xi_{i}^{*}}} \\{{\xi_{ir}\xi_{i}^{*}} \geq 0}\end{matrix},} } & (29)\end{matrix}$

where ∥w∥²=

w, w

, and the constant C determines the allowed amount of deviation of theerror larger than ε.

The above constrained optimization problem could be simplified bysolving a dual problem with Lagrange multipliers. The Lagrangianfunction is defined as:

$\begin{matrix}{L = {{\frac{1}{2}{w}^{2}} + {C{\sum\limits_{i = 1}^{l}( {\xi_{i} + \xi_{i}^{*}} )}} - {\sum\limits_{i = 1}^{l}( {{\gamma_{i}\xi_{i}} + {\gamma_{i}^{*}\xi_{i}^{*}}} )} - {\sum\limits_{i = 1}^{l}{\alpha_{i}( {ɛ + \xi_{i} - p_{i} + {\langle{w,x_{i}}\rangle} + b} )}} - {\sum\limits_{i = 1}^{l}{\alpha_{i}^{*}( {ɛ + \xi_{i}^{*} + p_{i} - {\langle{w,\ x_{i}}\rangle} - b} )}}}} & (30)\end{matrix}$

where γ_(i), γ_(i)*, α_(i), and α_(i)* are Lagrange multipliers, whichare assistant parameters. From the saddle point condition, the partialderivatives of the Lagrangian function have to vanish, hence

$\begin{matrix}{\frac{\partial L}{\partial b} = {{\sum\limits_{i = 1}^{l}( {\alpha_{i}^{*} - \alpha_{i}} )} = 0}} & (31) \\{\frac{\partial L}{\partial w} = {{w - {\sum\limits_{i = 1}^{l}{( {\alpha_{i} - \alpha_{i}^{*}} )x_{i}}}} = 0}} & (32) \\{\frac{\partial L}{\partial\xi_{i}} = {{C - \alpha_{i} - \gamma_{i}} = 0}} & (33) \\{\frac{\partial L}{\partial\xi_{i}^{*}} = {{C - \alpha_{i}^{*} - \gamma_{i}^{*}} = 0}} & (34)\end{matrix}$

Substitute Eq. (32) into Eq. (28), the support vector expansion isobtained as

$\begin{matrix}{{g(x)} = {{\sum\limits_{i = 1}^{l}{( {\alpha_{i} - \alpha_{i}^{*}} ){\langle{x_{i},x}\rangle}}} + b}} & (35)\end{matrix}$

The dot product

x_(i), x

in Eq. (35) can be construed to be a measure of similarity. For manyapplications, including meibography, a nonlinear kernel functionK(x_(i), x) can be constructed as a generalized measure of similarity,and the nonlinear support vector expansion is

$\begin{matrix}{{g(x)} = {{\sum\limits_{i = 1}^{l}{( {\alpha_{i} - \alpha_{i}^{*}} ){K( {x_{i},\ x} )}}} + b}} & (36)\end{matrix}$

Common kernel functions include a polynomial kernel such as K(x_(i),x_(j))=(x_(i)·x_(j)+1)^(p), a radial basis function kernel or a Gaussiankernel, such as K(x_(i), x_(j))=exp(−∥x_(i)−x_(j)∥²/2σ²), and a sigmoidkernel, such as K(x_(i), x_(j))=tanh(κx_(i)·x_(j)−δ).

The constant offset parameter b could be computed with theKarush-Kuhn-Tucker (KKT) conditions, which states that the productsbetween Lagrange multipliers and the constraints have to vanish,

a _(i)(ε+ξ_(i) −p _(i) +

w, x

+b)=0   (37)

a* _(i)(ε+ξ*_(i) +p _(i) −

w, x

−b)=0   (38)

(C−a _(i))ξ_(i)=0   (39)

(C−a* _(i))ξ*_(i)=0   (40)

Together with the analysis of the Lagrange multipliers, b could becomputed.

In the support vector regression analysis, each parameter of the inputeyelid characterization vector p=[p_(th0), p_(th1), p_(th2), p_(col),p_(mel), p_(car), p_(bil), p_(myo), p_(lip), p_(w), p_(bld), p_(o)] isestimated independently with Eq. (36). Repeat the regression process forall the characterization parameters, and the parameter estimation of theentire eyelid could be obtained in the inverse model.

Similar process of the inverse model could be done with other machinelearning methods, such as artificial neural networks, k-nearestneighbors algorithm, etc.

Note that the above forward model and inverse model are for an evertedeyelid from the conjunctival side. However, similar procedures could beapplied to an eyelid from the anterior skin side (cutaneous side) aswell.

Further, the multispectral and hyperspectral devices could also focus atthe eyelid margin, instead of the inner eyelid surface, as shown in FIG.9. The meibomian gland orifices of MGD patients could developinspissated lipids clogging, capping, pouting, retroplacement,obliteration, and there could be hyperkeratinization of the eyelidmargin, and potential crusting at the base of the eyelashes due toblepharitis. Multispectral and hyperspectral meibography could be usedto image meibomian gland distribution, meibomian gland orifices andeyelashes to investigate all of these pathological features.

FIG. 12 presents a flowchart representing method 100 of the disclosedmultispectral and hyperspectral meibography.

Method 100 includes (101) directing light from a broadband illuminatortoward at least a portion of an everted eyelid of a subject, wherein thelight covers visible and infrared spectra; (102) forming images of theeverted eyelid using an imaging system; (103) recording the images usinga detection system, wherein the detection system separates the imagesinto a plurality of spectral channels with optical filters or adispersive optical element, wherein the plurality of spectral channelscomprise at least one visible spectral channel and at least one infraredspectral channel; (104) displaying the recorded images to adjust arelative position of the subject to optimize image quality; (105)digitally processing the recorded images to obtain spatial and spectralinformation; (106) evaluating health of at least one meibomian gland ofthe everted eyelid by analyzing the spatial and spectral information. Insome embodiments, Method 100 further comprises (107) controlling thebroadband illuminator, the imaging system, and the detection system witha control system.

In a preferred embodiment, the step of digital processing 105 comprises(112) image preprocessing, which includes reflectance calibration,transmittance calibration, image smoothing, contrast enhancement, andremoval of illumination nonuniformity; (113) feature extraction, whereinthe original data dimensionality is reduced and the most importantsubset of the original data is obtained to extract relevant featureinformation of the everted eyelid; (114) segmentation, wherein a regionof interest (ROI) of meibomian glands is designated and separated in theimages; (115) parameter estimation, wherein characterization parametersof the everted eyelid are estimated; (116) evaluation, whereinpathological attributes and conditions are evaluated, including apercentage of meibomian gland coverage within the ROI, the meibomiangland atrophy, meibomian gland thickness and tortuosity.

In the following, three embodiments are disclosed. Embodiment 1 and 2are stationary multispectral and hyperspectral meibography devices forclinical use. Embodiment 3 is a portable, handheld multispectralmeibography device.

Embodiment 1

FIG. 14 presents one preferred embodiment, Embodiment 1, of the imagingsystem and the detection system of the multispectral and hyperspectralmeibography device. It is optimized in the spectral range of visible andinfrared spectra up to 950 nm for a sensor with a diagonal length of16.4 mm. The image F/#=8, and the system magnification=−0.5×.

The numerical details of Embodiment 1 are listed in Table 1, and thelength values are in units of mm.

TABLE 1 Surface Radius of Semi- Number curvature Thickness n_(d) V_(d)Aperture Object Infinity 180 1 23.5268 4 1.607381 56.6501 6.61 2−164.8068 4.2613 6.19 3 −38.5523 3 1.620053 36.4309 5.15 4 23.09850.8712 4.76 5-Stop Infinity 6.4642 4.75 6 89.6388 3 1.620053 36.43095.95 7 36.3950 4.1194 1.607381 56.6501 6.24 8 −33.2881 84.9892 6.499-Image Infinity 8.20

The object distance is 180 mm, and the overall length of Embodiment 1from Surface 1 to the image plane is 110.71 mm. From the object side tothe image side, Surfaces 1 to 2 is a positive lens, forming the firstpositive group 12 in FIG. 1. Surfaces 3 to 4 is a negative lens, formingthe negative group 13 in FIG. 1. Surface 5 is the aperture stop.Surfaces 6 to 8 is a positive doublet, forming the second positive group14 in FIG. 1. Further, the back focal length from Surface 8 to the imageplane Surface 9 is 84.99 mm, which is large enough to insert a dichroicbeamsplitter, an acousto-optic tunable filter or liquid crystal tunablefilter, if preferred. If optical filters are inserted, the designparameters could be readily adjusted to optimize the image quality.

Embodiment 2

FIG. 15 presents another preferred embodiment, Embodiment 2, of theimaging system and the detection system of the multispectral andhyperspectral meibography device. Embodiment 2 uses a dichroicbeamsplitter 30 as previously shown in FIG. 3(c). It is optimized in thespectral range of visible and infrared spectra up to 950 nm for a sensorwith a diagonal length of 16.4 mm The image F/#=8, and the systemmagnification=−0.5×.

The numerical details of Embodiment 2 are listed in Table 2, and thelength values are in units of mm.

TABLE 2 Surface Radius of Semi- Number curvature Thickness n_(d) V_(d)Aperture Object Infinity 180 1 20.5080 4 1.607381 56.6501 6.82 2−64.4761 4.3710 6.40 3 −27.7782 3 1.620053 36.4309 4.93 4 19.1004 1 4.425-Stop Infinity 13.5287 4.39 6 135.8552 3 1.620053 36.4309 6.90 733.7857 4 1.607381 56.6501 7.23 8 −33.2442 20 7.45 9 Infinity 1.051.458467 67.7963 10.80 10  Infinity 63.9590 7.33 11-Image Infinity 8.20

The object distance is 180 mm, and the overall length of Embodiment 2from Surface 1 to the image plane is 117.91 mm. From the object side tothe image side, Surfaces 1 to 2 is a positive lens, forming the firstpositive group 12 in FIG. 1. Surfaces 3 to 4 is a negative lens, formingthe negative group 13 in FIG. 1. Surface 5 is the aperture stop.Surfaces 6 to 8 is a positive doublet, forming the second positive group14 in FIG. 1. Surfaces 9 to 10 is a dichroic beamsplitter 30. Further,the back focal length from Surface 10 to the image plane Surface 11 is63.96 mm.

Embodiment 1 and 2 are mainly used for multispectral meibography.However, if the illumination system includes a tunable spectral filterto adjust the illumination wavelength in use, or a spectrometer systemis used in the detection system, Embodiment 1 and 2 could also be usedfor hyperspectral meibography.

This invention also discloses a handheld multispectral meibographydevice, as shown in FIG. 16, to conveniently inspect the meibomian glandmorphology and the eyelid margin health. A portable, handheld diagnosticdevice with a compact design has the advantages of flexible use indifferent environments, fast imaging and quick diagnostic feedback.

Embodiment 3

FIG. 16 presents one preferred embodiment, Embodiment 3, of the imagingsystem and the detection system of a handheld multispectral meibographydevice. It is optimized in the spectral range of visible and infraredspectra up to 1050 nm for a sensor with a diagonal length of 9 mm. Theimage F/#=8, and the system magnification=−0.3×.

The numerical details of Embodiment 3 are listed in Table 3, and thelength values are in units of mm.

TABLE 3 Surface Radius of Semi- Number curvature Thickness n_(d) V_(d)Aperture Object Infinity 50 1 7.1833 1.2006 1.607381 56.6501 2.48 2−24.1589 1.7601 2.29 3 −5.7314 1.0186 1.620053 36.4309 1.26 4 3.94490.7488 0.97 5-Stop Infinity 0.2086 0.84 6 11.3778 1.4998 1.62005336.4309 0.97 7 4.6125 1.4920 1.607381 56.6501 1.38 8 −4.5004 15.87651.63 9-Image Infinity 4.50

The object distance is 50 mm, and the overall length of Embodiment 3from Surface 1 to the image plane is 23.81 mm. This compact design issuitable for handheld requirement. From the object side to the imageside, Surfaces 1 to 2 is a positive lens, forming the first positivegroup 12 in FIG. 1. Surfaces 3 to 4 is a negative lens, forming thenegative group 13 in FIG. 1. Surface 5 is the aperture stop. Surfaces 6to 8 is a positive doublet, forming the second positive group 14 inFIG. 1. Further, the back focal length from Surface 8 to the image planeSurface 9 is 15.88 mm, which is large enough to insert a dichroicbeamsplitter or other optical filters, if preferred.

FIG. 17 presents one embodiment of the exterior of a handheldmeibographer. An illuminator 2 is connected to an exterior package 90.The imaging system and the detection system are inside of 90. Anoptional polarizer 23 (not shown in FIG. 17) could be placed in theanterior part of 2. A liquid-crystal display (LCD) screen 91 is used todisplay real time images to adjust the relative position of themeibographer and the patient to obtain sharp images of an evertedeyelid. Screen control buttons 92 are used to control the LCD. A handle93 is to be held by the operator. A record trigger 94 could be pressed,when a sharp image of the eyelid is in focus.

The invention has been described in detail with particular reference tocertain preferred embodiments thereof, but it will be understood thatvariations and modifications can be effected within the spirit and scopeof the invention.

All publications, patents and patent applications referred to herein areincorporated by reference in their entirety to the same extent as ifeach individual publication, patent or patent application wasspecifically and individually indicated to be incorporated by referencein its entirety in the present application.

What is claimed is:
 1. A method performed by a multispectral orhyperspectral meibomian gland imaging device, the method comprising:directing light from a broadband illuminator toward at least a portionof an everted eyelid of a subject, wherein said light covers visible andinfrared spectra; forming images of said everted eyelid using an imagingsystem; recording said images using a detection system, wherein saiddetection system separates said images into a plurality of spectralchannels with optical filters or a dispersive optical element, whereinsaid a plurality of spectral channels comprise at least one visiblespectral channel and at least one infrared spectral channel; displayingsaid recorded images to adjust a relative position of said subject tooptimize image quality; digitally processing said recorded images toobtain spatial and spectral information; evaluating health of at leastone meibomian gland of said everted eyelid by analyzing said spatial andspectral information.
 2. The method of claim 1, further comprising:controlling said broadband illuminator, said imaging system, and saiddetection system with a control system.
 3. The method of claim 1,wherein said illuminator comprises a polarizer with at least onepolarization state.
 4. The method of claim 1, wherein an analyzer isplaced in either said imaging system or said detection system, whereinsaid analyzer comprises at least one polarization state.
 5. The methodof claim 4, wherein said illuminator comprises a polarizer with at leastone polarization state, wherein at least one pair of polarization statesof said polarizer and said analyzer are chosen to minimize superficialsurface reflection of said everted eyelid.
 6. The method of claim 1,wherein said illuminator directs light toward said everted eyelid withdirect illumination without contacting said everted eyelid, whereinreflected and back scattered light from said everted eyelid is collectedinto said imaging system.
 7. The method of claim 1, wherein saidilluminator is placed behind a cutaneous side of said everted eyelidwith transillumination, wherein transmitted and forward scattered lightthrough said everted eyelid is collected into said imaging system. 8.The method of claim 1, wherein said illuminator comprises a tunablenarrow band filter or a dispersive optical element to adjust theillumination spectrum.
 9. The method of claim 1, wherein said detectionsystem is a spectrometer system.
 10. The method of claim 9, wherein saidspectrometer system comprises a member selected from a group consistingof a transmissive spectrometer, a Czerny-Turner spectrometer, a Littrowspectrometer, an Ebert-Fastie spectrometer, an Eagle spectrometer, aWadsworth spectrometer, a Dyson spectrometer, and a Fourier transformspectrometer.
 11. The method of claim 1, wherein said optical filtersare narrow band spectral filters or dichroic beamsplitters.
 12. Themethod of claim 1, wherein said dispersive optical element is a memberselected from a group consisting of a transmission grating, a reflectivediffraction grating, a holographic grating, a prism, and a diffractivelens.
 13. The method of claim 1, wherein said digital processingcomprises: image preprocessing; feature extraction; segmentation,wherein a region of interest of meibomian glands is designated andseparated in said images; parameter estimation, wherein characterizationparameters of said everted eyelid are estimated; evaluation, whereinpathological attributes and conditions are evaluated.
 14. The method ofclaim 13, wherein said image preprocessing comprises reflectancecalibration, transmittance calibration, image smoothing, contrastenhancement, and removal of illumination nonuniformity.
 15. The methodof claim 13, wherein said feature extraction comprises: generatingadditional digital spectral channels, by combination of at least two ofsaid plurality of spectral channels of said detection system; generatingfalse color images of said everted eyelid by selecting and combiningspectral channels out of said plurality of spectral channels of saiddetection system and said additional digital spectral channels.
 16. Themethod of claim 13, wherein said parameter estimation comprises amultilayer model of said everted eyelid, wherein each layer is modeledby said characterization parameters, wherein an inverse model based on aparametric method or a machine learning method is used for saidparameter estimation.
 17. The method of claim 16, wherein said machinelearning method comprises a member selected from a group consisting ofsupport vector machines, artificial neural networks, k-nearest neighborsalgorithm.
 18. The method of claim 1, wherein evaluating healthcomprises assessing an interconnection of palpebral vasculature and saidat least one meibomian gland by image fusion with said at least onevisible spectral channel and said at least one infrared spectral channel19. The method of claim 1, wherein evaluating health comprisesevaluating meibomian gland orifice obstruction and palpebral vasculatureof the everted eyelid with images of an eyelid margin.
 20. Amultispectral or hyperspectral meibomian gland imaging device,comprising: a broadband illuminator to direct light toward at least aportion of an everted eyelid of a subject, wherein said light coversvisible and infrared spectra; an imaging system to form images of saideverted eyelid; a detection system, wherein said detection systemseparates said images into a plurality of spectral channels with opticalfilters or a dispersive optical element, wherein said plurality ofspectral channels comprise at least one visible spectral channel and atleast one infrared spectral channel.